English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
 Local TagsRelease HistoryDetailsSummary
  Learning from Labeled and Unlabeled Data Using Random Walks

Zhou, D., & Schölkopf, B. (2004). Learning from Labeled and Unlabeled Data Using Random Walks. In C. Rasmussen, H. Bülthoff, B. Schölkopf, & M. Giese (Eds.), Pattern Recognition: 26th DAGM Symposium, Tübingen, Germany, August 30 - September 1, 2004 (pp. 237-244). Berlin, Germany: Springer.

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-F390-8 Version Permalink: https://hdl.handle.net/21.11116/0000-0005-523C-C
Genre: Conference Paper

Files

show Files

Locators

show
hide
Locator:
https://link.springer.com/content/pdf/10.1007%2F978-3-540-28649-3_29.pdf (Publisher version)
Description:
-
OA-Status:

Creators

show
hide
 Creators:
Zhou, D1, 2, Author           
Schölkopf, B1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: We consider the general problem of learning from labeled and unlabeled data. Given a set of points, some of them are labeled,
and the remaining points are unlabeled. The goal is to predict the
labels of the unlabeled points. Any supervised learning algorithm
can be applied to this problem, for instance, Support Vector
Machines (SVMs). The problem of our interest is if we can
implement a classifier which uses the unlabeled data information
in some way and has higher accuracy than the classifiers which use
the labeled data only. Recently we proposed a simple algorithm,
which can substantially benefit from large amounts of unlabeled
data and demonstrates clear superiority to supervised learning
methods. In this paper we further investigate the algorithm using
random walks and spectral graph theory, which shed light on the
key steps in this algorithm.

Details

show
hide
Language(s):
 Dates: 2004-09
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 2684
DOI: 10.1007%2F978-3-540-28649-3_29
 Degree: -

Event

show
hide
Title: 26th Annual Symposium of the German Association for Pattern Recognition (DAGM 2004)
Place of Event: Tübingen, Germany
Start-/End Date: 2004-08-30 - 2004-09-01

Legal Case

show

Project information

show

Source 1

show
hide
Title: Pattern Recognition: 26th DAGM Symposium, Tübingen, Germany, August 30 - September 1, 2004
Source Genre: Proceedings
 Creator(s):
Rasmussen, CE1, Editor           
Bülthoff, HH1, Editor           
Schölkopf, B1, Editor           
Giese, MA, Editor           
Affiliations:
1 Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794            
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 237 - 244 Identifier: ISBN: 978-3-540-22945-2

Source 2

show
hide
Title: Lecture Notes in Computer Science
Source Genre: Series
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 3175 Sequence Number: - Start / End Page: - Identifier: -